Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC—called a Tensor Processing Unit (TPU)— deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU’s deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, …) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters’ NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X – 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X – 80X higher. Moreover, using the GPU’s GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU. Index terms–DNN, MLP, CNN, RNN, LSTM, neural network, domain-specific architecture, accelerator

Machine learning powers many Google product features, from speech recognition in the Google app to Smart Reply in Inbox to search in Google Photos. While decades of experience have enabled the software industry to establish best practices for building and supporting products, doing so for services based upon machine learning introduces new and interesting challenges. Today, we announce the release of TensorFlow Serving, designed to address some of these challenges. TensorFlow Serving is a high performance, open source serving system for machine learning models, designed for production environments and optimized for TensorFlow.

TensorFlow Serving is ideal for running multiple models, at large scale, that change over time based on real-world data, enabling:
model lifecycle management
experiments with multiple algorithms
efficient use of GPU resources
TensorFlow Serving makes the process of taking a model into production easier and faster. It allows you to safely deploy new models and run experiments while keeping the same server architecture and APIs. Out of the box it provides integration with TensorFlow, but it can be extended to serve other types of models. Here’s how it works. In the simplified, supervised training pipeline shown below, training data is fed to the learner, which outputs a model

Once a new model version becomes available, upon validation, it is ready to be deployed to the serving system, as shown below.

TensorFlow Serving uses the (previously trained) model to perform inference – predictions based on new data presented by its clients. Since clients typically communicate with the serving system using a remote procedure call (RPC) interface, TensorFlow Serving comes with a reference front-end implementation based on gRPC, a high performance, open source RPC framework from Google. It is quite common to launch and iterate on your model over time, as new data becomes available, or as you improve the model. In fact, at Google, many pipelines run continuously, producing new model versions as new data becomes available.

TensorFlow Serving is written in C++ and it supports Linux. TensorFlow Serving introduces minimal overhead. In our benchmarks we recoded ~100,000 queries per second (QPS) per core on a 16 vCPU Intel Xeon E5 2.6 GHz machine, excluding gRPC and the TensorFlow inference processing time. We are excited to share this important component of TensorFlow today under the Apache 2.0 open source license. We would love to hear your questions and feature requests on Stack Overflow and GitHub respectively. To get started quickly, clone the code from github.com/tensorflow/serving and check out this tutorial. You can expect to keep hearing more about TensorFlow as we continue to develop what we believe to be one of the best machine learning toolboxes in the world. If you’d like to stay up to date, follow @googleresearch or +ResearchatGoogle, and keep an eye out for Jeff Dean’s keynote address at GCP Next 2016 in March.

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OpenStack Magnum

Magnum is an OpenStack API service developed by the OpenStack Containers Team making container orchestration engines such as Docker Swarm, Kubernetes, and Apache Mesos available as first class resources in OpenStack. Magnum uses Heat to orchestrate an OS image which contains Docker and Kubernetes and runs that image in either virtual machines or bare metal in a cluster configuration.